2018 Intelligent Sensing Summer School

29 August 2018 - 30 August 2018

A two-day event on advanced Intelligent Sensing topics, including sensing people, machine and deep learning, and image and audio processing. The School will cover sensing and machine learning topics, CIS PhD students will present one of their recently published papers during the student sessions, and a tutorial on Deep learning and IoT will introduce the participants to Keras for Deep Mining car driver behaviour, as follows:

Problem domain: A key application domain for IoT is Smart Transport. We will focus on classifying driving behaviour through analysing some of the data collected from sensors connected via a vehicle’s Controller Area Network or CAN bus and accessed via the vehicle’s On-Board Diagnostics (OBD)-II Interface. This data was collected as part of the Envirocar project. Our focus will be on the analysis the main factors that affect fuel consumption and for example determining if we can classify whether different car makes and to investigate if different driving behaviour lead to different classes of eco-friendly driving behaviour.

Method: To classify the car vehicle sensor data, we will use Keras, a high-level neural network API, to Google’s TensorFlow as a backend in order to implement some learning algorithms using a Deep neural network (DNN), a type of Artificial Neural Network (ANN) with multiple (hidden) layers between the input and output layers.

Workplan: This tutorial will start with a short overview of deep learning using Keras and will then implement a simple regression model. We will design and implement one simple DNN, either a Recurrent Neural Network (RNN) or a Convolutional Neural Network (CNN) and apply it to the OBD data.

Prerequisites: Participants must bring their own Laptop with Keras, TensorFlow and the Anaconda Python distribution pre-installed and they must already have some familiarity with programming in Python, and with the Keras overview and documentation.